Predicting Short-Term Mortality in Patients With Acute Pulmonary Embolism With Deep Learning
Our proposed mmDL model predicts short-term mortality in patients with acute PE with high accuracy and significantly outperforms the current standard PESI score.
Authors
- Mert İlker Hayıroğlu (ORCID: https://orcid.org/0000-0001-6515-7349)
- Mert Babaoğlu (ORCID: https://orcid.org/0000-0003-4544-9584)
- Nurgül Keser (ORCID: https://orcid.org/0000-0002-6482-9913)
- Vedat Çiçek (ORCID: https://orcid.org/0000-0002-3763-0570)
- Ahmet Lütfullah Orhan (ORCID: https://orcid.org/0000-0002-8617-3281)
- Ulaş Bağcı (ORCID: https://orcid.org/0000-0001-7379-6829)
- Ömer Ayten (ORCID: https://orcid.org/0000-0002-2275-4378)
- Vanshali Sharma (ORCID: https://orcid.org/0000-0003-0008-1579)
- Ahmet Öz (ORCID: https://orcid.org/0000-0003-0268-9641)
- Tufan Çınar (ORCID: https://orcid.org/0000-0001-8188-5020)
- Faysal Şaylık
- Almina Erdem (ORCID: https://orcid.org/0000-0003-4556-6027)
- Yalcin Tur
- Mehmet Uzun (ORCID: https://orcid.org/0000-0003-2999-489X)
- Şölen Taşlıçukur
Institutions
- Intel (United States) (US)
- Universidad CES (CO)
- Dr. Siyami Ersek Göğüs Kalp Ve Damar Cerrahisi Eğitim Ve Araştırma Hastanesi (TR)
- Stanford University (US)
Publication Details
- Journal
- Circulation Journal
- Published
- 2024-11-29
- DOI
- https://doi.org/10.1253/circj.cj-24-0630
- Citations
- 55
- Primary Topic
- Venous Thromboembolism Diagnosis and Management
- Type
- article
- Field-Weighted Citation Impact
- 30.21